@cogniformai/instructor-stream/dist/chunk-AL3KQNZS.cjs.map

Streaming-first structured data extraction from LLMs with real-time updates

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{ Effect, Layer, Stream } from 'effect'\nimport type { CompletionMeta, ActivePath, CompletedPaths } from './types.ts'\nimport { SchemaStream } from '@/utils/streaming-json-parser.ts'\nimport { readableStreamToAsyncGenerator } from '@/utils/streams.ts'\nimport { resolveSchema, type SchemaSource, type SchemaValidationMode } from '@/effect'\nimport {\n  SchemaResolutionError,\n  SnapshotValidationError,\n  StreamingError,\n  type StreamingPipelineError,\n} from '@/effect'\nimport type { SnapshotChunk } from '@/effect'\nimport type { Schema } from 'effect/Schema'\nimport * as SchemaApi from 'effect/Schema'\n\ntype ProviderMeta = Partial<CompletionMeta>\n\ntype ProviderStreamResult = {\n  readonly stream: ReadableStream<Uint8Array>\n  readonly meta?: ProviderMeta | (() => ProviderMeta)\n  readonly channelType?: string\n}\n\ntype HydrationRequest<A> = {\n  readonly schema: SchemaSource<A>\n  readonly provider: ProviderStreamResult\n  readonly validationMode?: SchemaValidationMode\n}\n\ntype StubRequest<A> = {\n  readonly schema: SchemaSource<A>\n  readonly defaultData?: Partial<A>\n}\n\ntype SnapshotHydratorService = {\n  readonly stream: <A>(\n    request: HydrationRequest<A>\n  ) => Stream.Stream<SnapshotChunk<A>, StreamingPipelineError, never>\n  readonly stub: <A>(\n    request: StubRequest<A>\n  ) => Effect.Effect<Partial<A>, StreamingPipelineError, never>\n}\n\n/**\n * Creates a validation function for Effect-based schema validation.\n *\n * @template A - The type of the schema to validate against\n * @param schema - Optional schema to use for validation. If undefined, returns undefined\n * @returns A validation function that returns a Promise<boolean> indicating validation success,\n *          or undefined if no schema was provided. The validation function decodes an unknown value\n *          using the provided schema and returns true if the Effect exits successfully.\n *\n * @example\n * ```typescript\n * const validator = validationForEffect(mySchema);\n * const isValid = await validator({ someData: 'value' }); // true or false\n * ```\n */\nconst validationForEffect = <A>(schema: Schema<A> | undefined) => {\n  if (!schema) {\n    return undefined\n  }\n  const decode = SchemaApi.decodeUnknown(schema)\n  return async (value: unknown): Promise<boolean> => {\n    const exit = await Effect.runPromiseExit(decode(value))\n    return exit._tag === 'Success'\n  }\n}\n\n/**\n * Creates a validation function for a Zod schema.\n *\n * @template A - The type that the Zod schema validates\n * @param schema - The Zod schema to use for validation, or undefined\n * @returns A validation function that returns a Promise<boolean>, or undefined if no schema is provided\n *\n * @remarks\n * The returned validation function performs asynchronous validation using the provided Zod schema's\n * `safeParseAsync` method. It returns `true` if the validation succeeds, `false` otherwise.\n *\n * @example\n * ```typescript\n * import { z } from 'zod';\n *\n * const userSchema = z.object({ name: z.string() });\n * const validate = validationForZod(userSchema);\n *\n * if (validate) {\n *   const isValid = await validate({ name: \"John\" }); // true\n * }\n * ```\n */\nconst validationForZod = <A>(schema: import('zod').ZodType<A> | undefined) => {\n  if (!schema) {\n    return undefined\n  }\n  return async (value: unknown): Promise<boolean> => {\n    const result = await schema.safeParseAsync(value)\n    return result.success\n  }\n}\n\n/**\n * Creates a streaming pipeline that hydrates and validates data against a schema.\n *\n * This function takes a schema, provider, and validation mode to create a stream that:\n * 1. Resolves the schema and extracts its Zod representation\n * 2. Parses incoming streaming data according to the schema structure\n * 3. Validates snapshots based on the specified validation mode\n * 4. Enriches each snapshot with metadata including validation status and path information\n *\n * @template A - The type of the data being hydrated\n * @param {HydrationRequest<A>} options - Configuration for the hydration process\n * @param {unknown} options.schema - The schema definition (must be resolvable to a Zod schema)\n * @param {object} options.provider - The data provider containing the source stream and metadata\n * @param {ReadableStream} options.provider.stream - The source stream of data to hydrate\n * @param {ProviderMeta | (() => ProviderMeta)} [options.provider.meta] - Metadata supplier for the provider\n * @param {string} [options.provider.channelType] - Optional channel type override\n * @param {'none' | 'on-complete' | 'final'} [options.validationMode='none'] - Validation strategy:\n *   - 'none': Skip validation, mark all snapshots as valid\n *   - 'on-complete': Validate when a schema path is completed\n *   - 'final': Only validate the final snapshot during stream flush\n * @returns {Stream.Stream<SnapshotChunk<A>, StreamingPipelineError, never>} An Effect Stream that emits validated snapshot chunks with metadata\n * @throws {SchemaResolutionError} When the schema cannot be resolved to a Zod schema\n * @throws {StreamingError} When the streaming pipeline encounters an error\n * @throws {SnapshotValidationError} When final validation fails in 'final' mode\n */\nconst hydrate = <A>({\n  schema,\n  provider,\n  validationMode = 'none',\n}: HydrationRequest<A>): Stream.Stream<SnapshotChunk<A>, StreamingPipelineError, never> => {\n  const resolved = resolveSchema(schema)\n  const zodSchema = resolved.zod\n  if (!zodSchema) {\n    throw new SchemaResolutionError({\n      message: `Schema '${resolved.name}' requires a Zod schema for streaming stub generation.`,\n    })\n  }\n\n  const validateWithEffect = validationForEffect(resolved.effect)\n  const validateWithZod = validationForZod(zodSchema)\n\n  /**\n   * A function that supplies metadata for the provider.\n   *\n   * If `provider.meta` is a function, it uses that function directly.\n   * Otherwise, it wraps the static `provider.meta` value (or an empty object if undefined)\n   * in a function that returns it.\n   *\n   * @returns A function that returns the provider's metadata\n   */\n  const metaSupplier =\n    typeof provider.meta === 'function' ?\n      (provider.meta as () => ProviderMeta)\n    : () => provider.meta ?? {}\n\n  let activePath: ActivePath = []\n  let completedPaths: CompletedPaths = []\n  let completedPathCount = 0\n  let pendingOnCompleteValidation = false\n  let lastSnapshot: Partial<A> | null = null\n  let lastValidationResult = true\n  /**\n   * Creates a new SchemaStream instance configured to parse and validate streaming data against a Zod schema.\n   *\n   * The stream operates in 'object' snapshot mode and provides null as default values for primitive types.\n   * Tracks the active parsing path and completed paths through the schema structure, triggering validation\n   * when new paths are completed.\n   *\n   * @remarks\n   * - Uses snapshot mode 'object' to capture intermediate parsing states\n   * - Null defaults are set for string, number, and boolean types during parsing\n   * - The onKeyComplete callback updates tracking variables when schema paths are completed\n   * - Triggers pending validation flag when new paths complete to enable validation of completed portions\n   *\n   * @see {@link SchemaStream} for the underlying stream implementation\n   *\n   * - Completed paths tracking (`_completedPaths`)\n   * - Channel type information (`_type`)\n   *\n   * @throws {StreamingError} When snapshot processing fails during transformation\n   * @throws {SnapshotValidationError} When final validation fails during the flush phase\n   */\n  const schemaStream = new SchemaStream(zodSchema, {\n    snapshotMode: 'object',\n    typeDefaults: {\n      string: null,\n      number: null,\n      boolean: null,\n    },\n    onKeyComplete: ({ activePath: nextActivePath, completedPaths: nextCompletedPaths }) => {\n      activePath = [...nextActivePath]\n      completedPaths = nextCompletedPaths.map((path) =>\n        Array.isArray(path) ? [...path] : ([] as CompletedPaths[number])\n      )\n      if (nextCompletedPaths.length > completedPathCount) {\n        pendingOnCompleteValidation = true\n        completedPathCount = nextCompletedPaths.length\n      }\n    },\n  })\n\n  const parser = schemaStream.parse({\n    handleUnescapedNewLines: true,\n  })\n\n  /**\n   * A TransformStream that handles validation and metadata enrichment for streaming snapshots.\n   *\n   * This stream processes partial snapshots of type `A` and transforms them into `SnapshotChunk<A>`\n   * objects with validation results and completion metadata.\n   *\n   * @remarks\n   * The stream supports three validation modes:\n   * - `'on-complete'`: Validates when a completion signal is received\n   * - `'none'`: Skips validation and marks all snapshots as valid\n   * - `'final'`: Validates only the final snapshot in the flush phase\n   *\n   * Validation can be performed using either Effect or Zod validators if provided.\n   * Each transformed chunk includes:\n   * - The original snapshot data\n   * - Validation status (`_isValid`)\n   * - Active path tracking (`_activePath`)\n   * - Completed paths tracking (`_completedPaths`)\n   * - Channel type information (`_type`)\n   *\n   * @throws {StreamingError} When snapshot processing fails during transformation\n   * @throws {SnapshotValidationError} When final validation fails during the flush phase\n   */\n  const validationStream = new TransformStream<Partial<A>, SnapshotChunk<A>>({\n    async transform(snapshot, controller) {\n      lastSnapshot = snapshot\n      try {\n        if (validationMode === 'on-complete' && pendingOnCompleteValidation) {\n          if (validateWithEffect) {\n            lastValidationResult = await validateWithEffect(snapshot)\n          } else if (validateWithZod) {\n            lastValidationResult = await validateWithZod(snapshot)\n          } else {\n            lastValidationResult = true\n          }\n          pendingOnCompleteValidation = false\n        } else if (validationMode === 'none') {\n          lastValidationResult = true\n        }\n\n        const baseMeta = metaSupplier() as CompletionMeta\n        const channelType = provider.channelType ?? baseMeta._type\n        const meta: CompletionMeta = {\n          ...baseMeta,\n          _isValid: validationMode === 'none' ? true : lastValidationResult,\n          _activePath: activePath,\n          _completedPaths: completedPaths,\n          ...(channelType !== undefined ? { _type: channelType } : {}),\n        }\n\n        controller.enqueue({\n          data: [snapshot],\n          _meta: meta,\n        })\n      } catch (error) {\n        controller.error(\n          new StreamingError({\n            message: 'Failed to process streaming snapshot',\n            cause: error,\n          })\n        )\n      }\n    },\n    async flush(controller) {\n      if (!lastSnapshot || validationMode !== 'final') {\n        return\n      }\n      try {\n        if (validateWithEffect) {\n          lastValidationResult = await validateWithEffect(lastSnapshot)\n        } else if (validateWithZod) {\n          lastValidationResult = await validateWithZod(lastSnapshot)\n        } else {\n          lastValidationResult = true\n        }\n      } catch (error) {\n        controller.error(\n          new SnapshotValidationError({\n            reason: 'Final validation failed',\n            issues: error,\n          })\n        )\n        return\n      }\n      const baseMeta = metaSupplier() as CompletionMeta\n      const channelType = provider.channelType ?? baseMeta._type\n      const meta: CompletionMeta = {\n        ...baseMeta,\n        _isValid: lastValidationResult,\n        _activePath: activePath,\n        _completedPaths: completedPaths,\n        ...(channelType !== undefined ? { _type: channelType } : {}),\n      }\n      controller.enqueue({\n        data: [lastSnapshot],\n        _meta: meta,\n      })\n    },\n  })\n\n  const validatedStream = provider.stream.pipeThrough(parser).pipeThrough(validationStream)\n  const generator = readableStreamToAsyncGenerator(validatedStream) as AsyncGenerator<\n    SnapshotChunk<A>\n  >\n\n  return Stream.fromAsyncIterable<SnapshotChunk<A>, StreamingPipelineError>(\n    generator,\n    (cause) =>\n      new StreamingError({\n        message: 'Streaming pipeline failed',\n        cause,\n      })\n  )\n}\n\n/**\n * Builds a partial stub object based on a provided schema and default data.\n *\n * This function resolves the given schema, extracts its Zod schema representation,\n * and generates a stub object with default values. The stub is created using\n * SchemaStream with null defaults for primitive types (string, number, boolean).\n *\n * @template A - The type of the object to be stubbed\n * @param {StubRequest<A>} params - The stub request configuration\n * @param {unknown} params.schema - The schema to resolve and use for stub generation\n * @param {A} [params.defaultData] - Optional default data to merge into the stub\n * @returns {Partial<A>} A partial object conforming to type A with stub values\n * @throws {SchemaResolutionError} When the schema cannot be resolved to a Zod schema\n *\n * @example\n * ```typescript\n * const stub = buildStub({\n *   schema: mySchema,\n *   defaultData: { name: 'John' }\n * });\n * ```\n */\nconst buildStub = <A>({ schema, defaultData }: StubRequest<A>): Partial<A> => {\n  const resolved = resolveSchema(schema)\n  const zodSchema = resolved.zod\n  if (!zodSchema) {\n    throw new SchemaResolutionError({\n      message: `Schema '${resolved.name}' requires a Zod schema to derive default streaming stubs.`,\n    })\n  }\n  const initialDefaults = defaultData as Record<string, unknown> | undefined\n  const schemaStream = new SchemaStream(zodSchema, {\n    defaultData: initialDefaults as never,\n    typeDefaults: {\n      string: null,\n      number: null,\n      boolean: null,\n    },\n  })\n  return schemaStream.getSchemaStub(zodSchema, initialDefaults as never) as Partial<A>\n}\n\n/**\n * Service for hydrating and constructing streaming pipelines and schema stubs.\n *\n * Provides two main operations:\n * - `stream`: Creates a streaming pipeline from a hydration request by unwrapping and hydrating the data\n * - `stub`: Builds a schema stub from a stub request for type validation purposes\n *\n * @remarks\n * Both operations handle errors by wrapping them in a `StreamingError` with descriptive messages.\n * The service is registered under the identifier 'instructor/streaming/SnapshotHydrator'.\n *\n * @example\n * ```typescript\n * const hydrator = yield* SnapshotHydrator;\n * const dataStream = hydrator.stream(request);\n * const schemaStub = yield* hydrator.stub(stubRequest);\n * ```\n */\nexport class SnapshotHydrator extends Effect.Service<SnapshotHydratorService>()(\n  'instructor/streaming/SnapshotHydrator',\n  {\n    effect: Effect.sync(() => ({\n      stream: <A>(request: HydrationRequest<A>) =>\n        Stream.unwrap(\n          Effect.try({\n            try: () => hydrate(request),\n            catch: (cause) =>\n              new StreamingError({\n                message: 'Unable to construct streaming pipeline',\n                cause,\n              }),\n          })\n        ),\n      stub: <A>(request: StubRequest<A>) =>\n        Effect.try({\n          try: () => buildStub(request),\n          catch: (cause) =>\n            new StreamingError({\n              message: 'Unable to construct schema stub',\n              cause,\n            }),\n        }),\n    })),\n  }\n) {}\n\nexport const SnapshotHydratorLayer: Layer.Layer<SnapshotHydrator> = SnapshotHydrator.Default\n","export async function* readableStreamToAsyncGenerator<T>(\n  stream: ReadableStream<T>\n): AsyncGenerator<T> {\n  const reader = stream.getReader()\n  try {\n    while (true) {\n      const { done, value } = await reader.read()\n      if (done) {\n        break\n      }\n      yield value as T\n    }\n  } finally {\n    reader.releaseLock()\n  }\n}\n","import { Effect, Stream, Fiber } from 'effect'\nimport * as Runtime from 'effect/Runtime'\nimport * as LanguageModel from '@effect/ai/LanguageModel'\nimport * as Response from '@effect/ai/Response'\nimport * as Prompt from '@effect/ai/Prompt'\nimport type * as Tool from '@effect/ai/Tool'\nimport type { SchemaSource, SchemaValidationMode } from './schema.ts'\nimport { SnapshotHydrator } from './core/runtime.ts'\nimport { StreamingError } from './errors.ts'\nimport type { CompletionMeta } from './core/types.ts'\n\n/**\n * Configuration object for streaming requests with schema validation.\n *\n * @template A - The type of the data structure that will be validated against the schema\n * @template Tools - Record of available tools that can be used during generation, defaults to any tool record\n *\n * @property schema - The schema source used for validating the streamed response data\n * @property prompt - Raw input prompt that will be processed by the language model\n * @property validationMode - Optional mode specifying how schema validation should be performed\n * @property options - Optional partial configuration for language model text generation, excluding the prompt property\n */\nexport type StreamRequest<A, Tools extends Record<string, Tool.Any> = Record<string, Tool.Any>> = {\n  readonly schema: SchemaSource<A>\n  readonly prompt: Prompt.RawInput\n  readonly validationMode?: SchemaValidationMode\n  readonly options?: Partial<Omit<LanguageModel.GenerateTextOptions<Tools>, 'prompt'>>\n}\n\n/**\n * Converts a stream of response parts into a provider-compatible ReadableStream with metadata.\n *\n * This function processes various types of stream parts (text deltas, reasoning deltas, finish events, errors)\n * and transforms them into a ReadableStream of bytes while collecting metadata such as token usage and\n * reasoning content.\n *\n * @template R - The environment type required by the stream\n * @template E - The error type that may be produced by the stream\n * @template Tools - A record of tool definitions\n *\n * @param parts - A stream of response parts to be processed\n *\n * @returns An Effect that produces an object containing:\n *   - `stream`: A ReadableStream<Uint8Array> containing encoded text deltas\n *   - `meta`: A function that returns completion metadata including token usage and reasoning content\n *\n * @remarks\n * - Text deltas are encoded and enqueued to the readable stream\n * - Reasoning deltas and endings are accumulated in a buffer\n * - Usage information is captured from finish events\n * - The stream is properly closed or errored based on the input parts\n * - The fiber can be interrupted via the ReadableStream's cancel method\n *\n * @throws {StreamingError} If the language model stream cannot be materialized\n */\nconst partsToProviderStream = <R, E, Tools extends Record<string, Tool.Any>>(\n  parts: Stream.Stream<Response.StreamPart<Tools>, E, R>\n) =>\n  Effect.gen(function* () {\n    const runtime = yield* Effect.runtime<R>()\n    const runFork = Runtime.runFork(runtime)\n    let usage: Response.Usage | undefined\n    let reasoning = ''\n    let fiber: Fiber.RuntimeFiber<unknown, unknown> | undefined\n    let finished = false\n    const encoder = new TextEncoder()\n    const readable = new ReadableStream<Uint8Array>({\n      start(controller) {\n        fiber = runFork(\n          Stream.runForEach(parts, (part) =>\n            Effect.sync(() => {\n              switch (part.type) {\n                case 'text-delta': {\n                  if (part.delta.length > 0) {\n                    controller.enqueue(encoder.encode(part.delta))\n                  }\n                  break\n                }\n                case 'reasoning-delta': {\n                  reasoning += part.delta\n                  break\n                }\n                case 'reasoning-end': {\n                  if ('text' in part && typeof part.text === 'string') {\n                    reasoning += part.text\n                  }\n                  break\n                }\n                case 'finish': {\n                  usage = part.usage\n                  break\n                }\n                case 'error': {\n                  if (!finished) {\n                    finished = true\n                    controller.error(part.error)\n                  }\n                  break\n                }\n              }\n            })\n          ).pipe(\n            Effect.catchAll((error) =>\n              Effect.sync(() => {\n                if (!finished) {\n                  finished = true\n                  controller.error(error)\n                }\n              })\n            ),\n            Effect.ensuring(\n              Effect.sync(() => {\n                if (!finished) {\n                  finished = true\n                  controller.close()\n                }\n              })\n            )\n          )\n        )\n      },\n      cancel() {\n        if (fiber) {\n          runFork(Fiber.interrupt(fiber))\n        }\n      },\n    })\n    const metaSupplier = (): Partial<CompletionMeta> => {\n      const meta: Partial<CompletionMeta> = {}\n      if (usage) {\n        meta.usage = {\n          prompt_tokens: usage.inputTokens ?? 0,\n          completion_tokens: usage.outputTokens ?? 0,\n          total_tokens: usage.totalTokens ?? (usage.inputTokens ?? 0) + (usage.outputTokens ?? 0),\n        }\n      }\n      const trimmedReasoning = reasoning.trim()\n      if (trimmedReasoning.length > 0) {\n        meta.thinking = trimmedReasoning\n      }\n      return meta\n    }\n    return { stream: readable, meta: metaSupplier }\n  }).pipe(\n    Effect.catchAll((cause) =>\n      Effect.fail(\n        new StreamingError({\n          message: 'Unable to materialize language model stream',\n          cause,\n        })\n      )\n    )\n  )\n\n/**\n * Streams structured data extraction from a language model response.\n *\n * This function takes a stream request containing a prompt and schema, sends it to the language model,\n * and returns a stream of hydrated objects that conform to the provided schema. The stream processes\n * the language model's text output in real-time, validating and transforming it into structured data.\n *\n * @template A - The type of the structured data to extract, inferred from the schema\n * @param request - The stream request configuration\n * @param request.prompt - The prompt to send to the language model\n * @param request.schema - The schema defining the structure of the expected output\n * @param request.options - Optional additional options to pass to the language model\n * @param request.validationMode - Optional validation mode for the hydrator\n *\n * @returns A Stream that emits hydrated objects conforming to the schema\n *\n * @example\n * ```TypeScript\n * const userStream = stream({\n *   prompt: \"Extract user information\",\n *   schema: z.object({ name: z.string(), age: z.number() })\n * })\n * ```\n */\nexport const stream = <A>(request: StreamRequest<A>) =>\n  Stream.unwrap(\n    Effect.gen(function* () {\n      const languageModel = yield* LanguageModel.LanguageModel\n      const hydrator = yield* SnapshotHydrator\n      const parts = languageModel.streamText({\n        prompt: request.prompt,\n        ...(request.options ?? {}),\n      })\n      const providerResult = yield* partsToProviderStream(parts)\n      return hydrator.stream({\n        schema: request.schema,\n        provider: {\n          stream: providerResult.stream,\n          ...(providerResult.meta ? { meta: providerResult.meta } : {}),\n        },\n        ...(request.validationMode !== undefined ? { validationMode: request.validationMode } : {}),\n      })\n    })\n  )\n\n/**\n * Creates a stub value based on the provided schema with optional default data.\n *\n * This function generates a stub instance by hydrating a schema through the SnapshotHydrator.\n * The stub can be initialized with partial default data if provided.\n *\n * @template A - The type of the value to be created from the schema\n * @param schema - The schema source defining the structure of the value\n * @param defaultData - Optional partial data to initialize the stub with\n * @returns An Effect that produces a stubbed value conforming to the schema\n *\n * @example\n * ```TypeScript\n * const userSchema = S.struct({ name: S.string, age: S.number });\n * const userStub = stub(userSchema, { name: \"John\" });\n * ```\n */\nexport const stub = <A>(schema: SchemaSource<A>, defaultData?: Partial<A>) =>\n  Effect.flatMap(SnapshotHydrator, (hydrator) =>\n    hydrator.stub({\n      schema,\n      ...(defaultData !== undefined ? { defaultData } : {}),\n    })\n  )\n"]}